Research on Revision of Training Program of the Economics and Management Specialties Based on the Training Mode--- A Case Study from Changchun University of Science and Technology

Qi, Dianwei; Yu, Shili

doi:10.5430/ijhe.v2n3p62

Cited by 5 publications

(5 citation statements)

References 1 publication

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“…In recent years, with the advancement of science and technology and the development of society, people are more and more interested in the research of artificial intelligence. Among them, the automatic recognition of expressions is a key part of the field of artificial intelligence, and it has attracted more and more attention [1]. However, due to the complexity and subtle changes in expression, the recognition of the same person's expression may be different due to changes in image illumination, image posture, gender, age, facial hair, and glasses worn.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Research on professional talent training technology based on multimedia remote image analysis

Bin

Liang

et al. 2019

J Image Video Proc.

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In distance vocational education, teachers need to analyze according to the expression status of different students, so as to make corresponding training in training to improve training efficiency. At present, there are certain problems in the remote expression recognition of professional personnel. Based on this, this study analyzes the facial expression image and uses the wavelet transform algorithm to process the face image in complex lighting environment, thus improving the online transmission effect of the image. After that, this study uses orthogonal projection algorithm for face recognition. In addition, this paper enhances LBP features by dividing the original image into four images by wavelet decomposition. At the same time, in order to prevent the over-characteristics from reducing the classification accuracy and real-time calculation, this paper uses the PCA principal component analysis method to select the feature subset with the largest discrimination. Finally, through SVM, this article has done experiments on JAFFE facial expression database. The experimental results show that the proposed method has a significant improvement in the correct rate compared with the traditional LBP feature classification method and can improve the theoretical reference for subsequent related research.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Research on professional talent training technology based on multimedia remote image analysis

Bin

Liang

et al. 2019

J Image Video Proc.

View full text Add to dashboard Cite

show abstract

“…Addressing the inherent limitations in the APF method [21], the approach in [22] added virtual target points to modify the force equilibrium at those specific locations. Reference [23] introduced random disturbance components into the attractive potential field. Ref.…”

Section: Introductionmentioning

confidence: 99%

Integrated the Artificial Potential Field with the Leader–Follower Approach for Unmanned Aerial Vehicles Cooperative Obstacle Avoidance

Zhang,

Chen,

Chen

et al. 2024

Mathematics

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For the formation and obstacle avoidance challenges of UAVs (unmanned aerial vehicles) in complex scenarios, this paper proposes an improved collaborative strategy based on APF (artificial potential field). This strategy combines graph theory, the Leader–Follower method, and APF. Firstly, we used graph theory to design formation topology and dynamically adjust the distances between UAVs in real time. Secondly, we introduced APF to avoid obstacles in complicated environments. This algorithm innovatively integrates the Leader–Follower formation method. The design of this attractive field is replaced by the leader’s attraction to the followers, overcoming the problem of unreachable targets in APF. Meanwhile, the introduced Leader–Follower mode reduces information exchange within the swarm, realizing a more efficient “few controlling many” paradigm. Afterwards, we incorporated rotational force to assist the swarm in breaking free from local minima. Ultimately, the stability of the integrated formation strategy was demonstrated using Lyapunov functions. The feasibility and effectiveness of the proposed strategy were validated across multiple platforms.

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“…Through the literature research, we found that the above methods can perform ear recognition, but compared with traditional ear recognition algorithms, the ear recognition algorithm based on deep learning has certain advantages [21]. In deep learning, the singlestage network is faster than the two-stage network.…”

Section: Introductionmentioning

confidence: 99%

Lightweight YOLOv5s Human Ear Recognition Based on MobileNetV3 and Ghostnet

et al. 2023

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Ear recognition is a biometric identification technology based on human ear feature information, which can not only detect the human ear in the picture but also determine whose human ear it is, so human identity can be verified by human ear recognition. In order to improve the real-time performance of the ear recognition algorithm and make it better for practical applications, a lightweight ear recognition method based on YOLOv5s is proposed. This method mainly includes the following steps: First, the MobileNetV3 lightweight network is used as the backbone network of the YOLOv5s ear recognition network. Second, using the idea of the Ghostnet network, the C3 module and Conv module in the YOLOv5s neck network are replaced by the C3Ghost module and GhostConv module, and then the YOLOv5s-MG ear recognition model is constructed. Third, three distinctive human ear datasets, CCU-DE, USTB, and EarVN1.0, are collected. Finally, the proposed lightweight ear recognition method is evaluated by four evaluation indexes: mAP value, model size, computational complexity (GFLOPs), and parameter quantity (params). Compared with the best results of YOLOv5s, YOLOv5s-V3, YOLOv5s-V2, and YOLOv5s-G methods on the CCU-DE, USTB, and EarVN1.0 three ear datasets, the params, GFLOPS, and model size of the proposed method YOLOv5s-MG are increased by 35.29%, 38.24%, and 35.57% respectively. The FPS of the proposed method, YOLOv5s-MG, is superior to the other four methods. The experimental results show that the proposed method has the performance of larger FPS, smaller model, fewer calculations, and fewer parameters under the condition of ensuring the accuracy of ear recognition, which can greatly improve the real-time performance and is feasible and effective.

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Research on Revision of Training Program of the Economics and Management Specialties Based on the Training Mode--- A Case Study from Changchun University of Science and Technology

Cited by 5 publications

References 1 publication

RETRACTED ARTICLE: Research on professional talent training technology based on multimedia remote image analysis

RETRACTED ARTICLE: Research on professional talent training technology based on multimedia remote image analysis

Integrated the Artificial Potential Field with the Leader–Follower Approach for Unmanned Aerial Vehicles Cooperative Obstacle Avoidance

Lightweight YOLOv5s Human Ear Recognition Based on MobileNetV3 and Ghostnet

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